GPU compute for AI, machine learning and more
On-demand GPUs for parallel processing workloads such as machine learning, scientific computing, and video processing.
GPU compute for AI, machine learning and more
On-demand GPUs for parallel processing workloads such as machine learning, scientific computing, and video processing.
GPU
Scientists, artists, and engineers need access to significant parallel computational power. Offering GPU-optimized virtual machines accelerated by the NVIDIA Quadro RTX 6000, harnessing the power of CUDA, Tensor, and RT cores to execute complex processing, deep learning, and ray tracing workloads.
On-demand GPU
Between purchasing, installing, and maintaining GPUs, the cost of ownership is often high. Leverage the power of GPUs while benefiting from the main value proposition of cloud: turning a capital expense into an operating expense.
Market Leading Hardware
GPUs are NVIDIA Quadro RTX 6000 units, currently considered one of the best in market GPUs. With CUDA, Tensor, and RT cores in each unit, these GPUs support any use cases associated with parallel processing, deep learning, or ray tracing.
Simplifying Complex Use Cases
You focus on your core competencies, we’ll worry about the hardware that makes them possible. Cloud GPUs remove the barrier of entry for complex use cases such as machine learning, AI, and video streaming.
Multiple Plan Types
One GPU card isn’t enough for your projected workloads? Not a problem. GPU plans offer up to four cards per instance, depending on how much horsepower you need.
Specifications
Cloud GPU Overview
Quadro RTX 6000 Specs
| CUDA Cores (Parallel-Processing) | 4,608 |
|---|---|
| Tensor Cores (Machine & Deep Learning) | 576 |
| RT Cores (Ray Tracing) | 72 |
| GPU Memory | 24 GB GDDR6 |
| RTX-OPS | 84T |
| Rays Cast | 10 Giga Rays / Sec |
| FP32 Performance | 16.3 TFLOPS |
Leading Price-Performance
GPU Benchmarking: Independent benchmarking firm, Cloud Spectator, found our GPUs deliver the best performance per dollar when compared to AWS, Azure, and GCP.
Particle Physics Performance Per Dollar
Also recommended: Infographic: The Business Case for GPUs in the Cloud
Dedicated + RTX6000 GPU Plans
- Grid
| RAM | CPUs | SSD Storage | GPU Cards | Transfer | Network In | Network Out | Monthly | Hourly | |
|---|---|---|---|---|---|---|---|---|---|
| 32 GB | 8 | 640 GB | 1 | 16 TB | 40 Gbps | 10 Gbps | $1,000 | $1.50 | Sign Up |
| 64 GB | 16 | 1280 GB | 2 | 20 TB | 40 Gbps | 10 Gbps | $2,000 | $3.00 | Sign Up |
| 96 GB | 20 | 1920 GB | 3 | 20 TB | 40 Gbps | 10 Gbps | $3,000 | $4.50 | Sign Up |
| 128 GB | 24 | 2560 GB | 4 | 20 TB | 40 Gbps | 10 Gbps | $4,000 | $6.00 | Sign Up |
Try Our GPUs
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The Compute Instance Family
Match your application use case with the right compute resource.
Shared CPU Cores
Related Content
Craft of Code:
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Content Resources
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Linode Cloud GPUs Beat AWS For Performance, Pricing
According to a Cloud Spectator benchmarking, Linode Cloud GPUs offer the best performance in the industry, outperforming behemoths like AWS. -
The Business Case for GPUs in the Cloud
Once confined to personal computers, GPUs are essential to accelerate highly-customized apps and business workloads in the cloud. -
GPU Price-Performance Benchmarking
Cloud Spectator evaluated the performance of GPU-based virtual machines from AWS, Azure, GCP, and Linode.
Guides
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Getting Started with Linode GPU Instances
This guide will help you get your Linode GPU Instance up and running on a number of popular distributions. -
Cloud GPUs Tutorial from sentdex
An overview of the current cloud GPU providers and a tutorial on making good use of these servers. -
Plan Types – Choosing a Compute Instance Type and Plan
This guide provides an overview of all Linode instance types and plans, their corresponding use cases, and how to choose which one is right for you.
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